Chapter 2 Deterministic optimization of distillation processes
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José A. Caballero
Abstract
In this chapter, we present an overview of deterministic optimization methods for the design of column sequences for separating zeotropic mixtures. First, we focus on models developed in the last years of the twentieth century that deal with sequences formed by conventional columns and the sharp separation of consecutive key components. The extension to models for the sharp separation of components while allowing sloppy separations (nonconsecutive key components) is untimely related to thermally coupled distillation; therefore then we do a discussion on the structural characteristics that characterize thermally coupled distillation and present deterministic models for optimizing these sequences. Column sequencing is usually based on shortcut models, which are usually good enough, especially if we are interested in sequence comparison. However, in some situations rigorous models are mandatory; therefore we introduce the main models developed for the optimization of distillation columns. Using generalized disjunctive programming as a modeling framework, we show that it is possible to formulate different models with varying levels of complexity (equilibrium or transport-based, or even including reactions) without altering the model structure. This enables, for instance, the use of advanced commercial simulators within hybrid optimization models.
Abstract
In this chapter, we present an overview of deterministic optimization methods for the design of column sequences for separating zeotropic mixtures. First, we focus on models developed in the last years of the twentieth century that deal with sequences formed by conventional columns and the sharp separation of consecutive key components. The extension to models for the sharp separation of components while allowing sloppy separations (nonconsecutive key components) is untimely related to thermally coupled distillation; therefore then we do a discussion on the structural characteristics that characterize thermally coupled distillation and present deterministic models for optimizing these sequences. Column sequencing is usually based on shortcut models, which are usually good enough, especially if we are interested in sequence comparison. However, in some situations rigorous models are mandatory; therefore we introduce the main models developed for the optimization of distillation columns. Using generalized disjunctive programming as a modeling framework, we show that it is possible to formulate different models with varying levels of complexity (equilibrium or transport-based, or even including reactions) without altering the model structure. This enables, for instance, the use of advanced commercial simulators within hybrid optimization models.
Chapters in this book
- Frontmatter I
- Contents V
- List of contributing authors VII
- Chapter 1 Optimization and its importance for chemical engineers: challenges, opportunities, and innovations 1
- Chapter 2 Deterministic optimization of distillation processes 25
- Chapter 3 Optimal design of process energy systems integrating sustainable considerations 79
- Chapter 4 Metaheuristics for the optimization of chemical processes 113
- Chapter 5 Surrogate-based optimization techniques for process systems engineering 159
- Chapter 6 Data-driven techniques for optimal and sustainable process integration of chemical and manufacturing systems 215
- Chapter 7 Applications of Bayesian optimization in chemical engineering 255
- Chapter 8 Sensitivity assessment of multi-criteria decision-making methods in chemical engineering optimization applications 283
- Chapter 9 Hybrid optimization methodologies for the design of chemical processes 305
- Chapter 10 Optimization under uncertainty in process systems engineering 343
- Chapter 11 Optimal control of batch processes in the continuous time domain 379
- Chapter 12 Supply chain optimization for chemical and biochemical processes 401
- Chapter 13 Future insights for optimization in chemical engineering 425
- Index 445
Chapters in this book
- Frontmatter I
- Contents V
- List of contributing authors VII
- Chapter 1 Optimization and its importance for chemical engineers: challenges, opportunities, and innovations 1
- Chapter 2 Deterministic optimization of distillation processes 25
- Chapter 3 Optimal design of process energy systems integrating sustainable considerations 79
- Chapter 4 Metaheuristics for the optimization of chemical processes 113
- Chapter 5 Surrogate-based optimization techniques for process systems engineering 159
- Chapter 6 Data-driven techniques for optimal and sustainable process integration of chemical and manufacturing systems 215
- Chapter 7 Applications of Bayesian optimization in chemical engineering 255
- Chapter 8 Sensitivity assessment of multi-criteria decision-making methods in chemical engineering optimization applications 283
- Chapter 9 Hybrid optimization methodologies for the design of chemical processes 305
- Chapter 10 Optimization under uncertainty in process systems engineering 343
- Chapter 11 Optimal control of batch processes in the continuous time domain 379
- Chapter 12 Supply chain optimization for chemical and biochemical processes 401
- Chapter 13 Future insights for optimization in chemical engineering 425
- Index 445
